Deployment Problem Research Articles

Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization

This paper is mainly devoted to studying the deployment problem of a multi-static radar system (MSRS) within a non-connected deployment region using multi-objective particle swarm optimization (MOPSO). By modeling and reformulating the problem, it can be represented as a multi-objective mixed integer programming (MOMIP), which eliminates the need for additional constraints. To enhance the algorithm performance, integer variables and continuous ones are treated separately employing multiple velocity formulas. The velocity formulas for integer variables are modified using the sigmoid function and genetic operation, leading to the proposal of two MSRS deployment algorithms, namely MOPSO-Sigmoid and MOPSO-Gene. To evaluate the performance of the proposed algorithms, they are compared with two existing MOPSO-based algorithms. The first algorithm is the MSRS deployment algorithm for the non-connected deployment region that addresses the additional constraint problem model. The second algorithm is based on an existing conventional MOPSO algorithm and addresses the equivalent MOMIP problem model. A numerical study demonstrates that MOPSO-Sigmoid and MOPSO-Gene exhibit promising efficiency and effectiveness.

Multi-Strategy Bald Eagle Search Algorithm Embedded Orthogonal Learning for Wireless Sensor Network (WSN) Coverage Optimization

Coverage control is a fundamental and critical issue in plentiful wireless sensor network (WSN) applications. Aiming at the high-dimensional optimization problem of sensor node deployment and the complexity of the monitoring area, an orthogonal learning multi-strategy bald eagle search (OLMBES) algorithm is proposed to optimize the location deployment of sensor nodes. This paper incorporates three kinds of strategies into the bald eagle search (BES) algorithm, including Lévy flight, quasi-reflection-based learning, and quadratic interpolation, which enhances the global exploration ability of the algorithm and accelerates the convergence speed. Furthermore, orthogonal learning is integrated into BES to improve the algorithm’s robustness and premature convergence problem. By this way, population search information is fully utilized to generate a more superior position guidance vector, which helps the algorithm jump out of the local optimal solution. Simulation results on CEC2014 benchmark functions reveal that the optimization performance of the proposed approach is better than that of the existing method. On the WSN coverage optimization problem, the proposed method has greater network coverage ratio, node uniformity, and stronger optimization stability when compared to other state-of-the-art algorithms.

Non-linear programming model proposal for mutual support distance of air defence systems

With the advancements in technology, the deployment of air defence systems required for the defence of a country after procurement is considered as an important problem in terms of defence effectiveness. It is seen that criteria such as coverage, strike effectiveness and logistics network are taken into account while deploying. In addition, principles such as remote countermeasures, defence in depth and all-round defence are also taken into account. In this study, two different deployment models are proposed for point and area air defence. In this model, the optimum deployment distance at which other systems can support each other in addition to a deployed air defence system is determined. In the nonlinear target programming model proposed for point air defence, the number of missiles is considered to be reasonably balanced for perimeter defence, while for area defence, the nonlinear optimization problem is addressed by calculating the equally important coverage and strike effectiveness criteria values. In this study, it is observed that the approach based on mutual support distance has been used for the first time in the defence literature. According to the results obtained, it is observed that this proposed model for determining the mutual support distance solves the deployment problem optimally.

Node Deployment of WSN Based on Drones

In order to solve the problem of WSN node deployment being unable to achieve relatively orderly and efficient deployment in some specific scenarios, this study refers to the specific development of WSN coverage technology at home and abroad, and hopes to create a basic and universal coverage system based on existing technology, in order to face various emergency situations in reality.

Graph Neural Network-Based Node Deployment for Throughput Enhancement.

The recent rapid growth in mobile data traffic entails a pressing demand for improving the throughput of the underlying wireless communication networks. Network node deployment has been considered as an effective approach for throughput enhancement which, however, often leads to highly nontrivial nonconvex optimizations. Although convex-approximation-based solutions are considered in the literature, their approximation to the actual throughput may be loose and sometimes lead to unsatisfactory performance. With this consideration, in this article, we propose a novel graph neural network (GNN) method for the network node deployment problem. Specifically, we fit a GNN to the network throughput and use the gradients of this GNN to iteratively update the locations of the network nodes. Besides, we show that an expressive GNN has the capacity to approximate both the function value and the gradients of a multivariate permutation-invariant function, as a theoretic support to the proposed method. To further improve the throughput, we also study a hybrid node deployment method based on this approach. To train the desired GNN, we adopt a policy gradient algorithm to create datasets containing good training samples. Numerical experiments show that the proposed methods produce competitive results compared with the baselines.

Unlocking potential for a circular bioeconomy transition through digital innovation, lean manufacturing and green practices: a review

PurposeToday’s businesses are looking for a circular bioeconomy (CBE) to develop a sustainable manufacturing process as industrial operations result in significant amounts of waste materials and the depletion of natural sources. The industry commonly applies techniques such as lean manufacturing (LM), digital innovations (DI) and green practices (GP) for operational and quality improvement. However, publications explaining how these technologies enable the CBE transition are scarce. This study examines CBE components, common practices of each technology facilitating the CBE transition, problems of solitary technology deployment as well as coupling technologies for the CBE transition.Design/methodology/approachA scoping review was conducted to analyse previous studies in this new field. The data collection is in a quantitative manner, but the data synthesis process follows a similar method of synthesising data in the grounded theory method, which includes familiarisation with the data, open-coding and finalisation of the themes.FindingsCritical components of CBE were identified as biobased goods, industry symbiosis, material resource efficiency, renewable energy, product lifecycle and sharing economy. GP is the most prominent in moderating the CBE transition. We identify each technology has coupled relationships (Lean-4.0, Green-Lean and Green-4.0) technologies facilitated by the circularity concept, which form the core pillars of enablers and advance the CBE paradigm.Research limitations/implicationsThis study demonstrates that combining lean principles with green technology and digital technologies can effectively decrease waste and resource usage in biobased manufacturing processes, therefore endorsing the concept of resource efficiency in circular bioeconomy models.Practical implicationsThe results allow entrepreneurs to strategically incorporate different existing technologies to meet CBE fundamental objectives by initiating it with dual technologies and facilitate industry professionals and regulators to support the improvement of environmental sustainability performance in the manufacturing industry. The management will be able to focus on the common practices across the technologies, which have a dual benefit for both operational and environmental performance.Originality/valueThe paper makes the first attempt to present the synergic impact of the three quality management technologies on a new concept of sustainability, CBE.

Striking the perfect balance: Multi-objective optimization for minimizing deployment cost and maximizing coverage with Harmony Search

In the Internet of Things (IoT) era, wireless sensor networks play a critical role in communication systems. One of the most crucial problems in wireless sensor networks is the sensor deployment problem, which attempts to provide a strategy to place the sensors within the surveillance area so that two fundamental criteria of wireless sensor networks, coverage and connectivity, are guaranteed. In this paper, we look to solve the multi-objective deployment problem so that area coverage is maximized and the number of nodes used is minimized. Since Harmony Search is a simple yet suitable algorithm for our work, we propose Harmony Search algorithm along with various enhancement proposals, including heuristic initialization, random sampling of sensor types, weighted fitness evaluation, and using different components in the fitness function, to provide a solution to the problem of sensor deployment in a heterogeneous wireless sensor network where sensors have different sensing ranges. On top of that, the probabilistic sensing model is used to reflect how the sensors work realistically. We also provide the extension of our solution to 3D areas and propose a realistic 3D dataset to evaluate it. The simulation results show that the proposed algorithms solve the area coverage problem more efficiently than previous algorithms. Our best proposal demonstrates significant improvements in coverage ratio by 10.20% and cost saving by 27.65% compared to the best baseline in a large-scale evaluation.

Reconfigurable topology assessment for efficient allocation of photovoltaic generators in unbalanced power delivery networks

The three-phase power delivery systems are reliable and efficient means of delivering electric power to customer node points. However, the unequal distribution of loads over the three phases leads to unbalance voltage between phases, resulting in increased power losses in distribution transformers and lines. It is highly challenging to install photovoltaic (PV) generators in unbalanced power distribution networks (PDNs) to restore power supply efficiency. This paper proposes dynamic reconfiguration (DR) in unbalanced PDNs to foster efficient integration of PV generators and improve system performance. Since the demand for electricity at residential node points and the PV-generated power varies with the season, this study proposes a seasonal DR of the PDNs. A framework with multiple objectives has been proposed for the DR-coupled PV deployment problem. A novel value-adaptive weight-aggregated (VAWA) grey-wolf optimizer (GWO) has been synthesized to contribute to system performance enhancement. The proposed strategy was validated and established through comparative investigations on an Indian 19-bus and modified IEEE 123-bus unbalanced radial distribution systems (URDSs).

A multi-objective Roadside Units deployment strategy based on reliable coverage analysis in Internet of Vehicles

The deployment of Roadside Units (RSUs) in the Cellular-Vehicle to Everything enabled Internet of Vehicles is crucial for the transition from individual intelligence of vehicles to collective intelligence of vehicle-road collaboration. In this paper, we focus on improving the adaptability of RSU deployment to real scenarios, and optimizing deployment costs and vehicle-oriented service performance. The RSU deployment problem is modeled as a Multi-objective Optimization Problem (MOP), with a cost model integrating the purchase and installation costs, and a service-oriented Quality of Service (QoS) model adopting the total time the RSUs cover the vehicles as the evaluation metric. Specifically, we propose an RSU reliable coverage analysis method based on Packet Delivery Ratio model to estimate the coverage distances in different scenarios, which will be used in QoS calculation. Then, an evolutionary RSU deployment algorithm is designed to solve the MOP. The performance of the proposed method is simulated and discussed in real road network and dynamic scenarios. The results prove that our method outperforms the baseline method in terms of significant cost reduction and total coverage time improvement.

Ship deployment problem with green technology adoption for an inland river carrier under non-identical streamflow and speed limits

The maritime industry is currently experiencing a shift towards green corridors. The inland waterway is the main transportation corridor with a relatively low adoption of green technologies, which produces heavy air pollution in inland cities. This paper investigates the ship deployment problem for an inland river carrier considering the adoption of green fuel and green technologies for a given ship fleet. A non-linear programming (NLP) model is proposed to jointly optimize green technology adoption, sailing speed, and routing problems while considering two unique characteristics of inland river shipping, including the non-identical streamflow and the speed limits on each shipping leg. The analytical propositions reveal the optimal operation strategy of the inland river ship fleet, and a column generation-based algorithm is further designed to solve the proposed model. By jointly investigating the ship operation strategy and sulphur emissions, we find the following management insights: Firstly, the ship would have a lower willingness to invest in shore power (scrubber) if it installed scrubber (shore power). The higher streamflow and looser speed limits would reduce the spillover effect of green technology adoption. Secondly, emissions will spillover from ports to river legs if only shore power is provided. Our findings provide valuable insights for policymakers to promote complete green technology adoption to achieve comprehensive abatement of the entire inland river.

Distributed service function chaining in NFV-enabled networks: A game-theoretic learning approach

In network function virtualization (NFV), Service Function Chaining (SFC) provides an ordered sequence of virtual network functions (VNFs) and subsequent steering of traffic flows through them to cater to end-to-end services. This paper addresses the NP-hard problem of minimum cost SFC deployment to support customer services that access the carrier network’s NFV infrastructure (NFVI) through some edge routers. To determine the mappings of VNFs to physical servers, a challenging aspect would be the inter-server latencies that may fluctuate over time because of the sharing nature of cloud data centers. To construct the SFC, we come up with three different formulations, each corresponding to a different informational assumption about the link latencies: First, a centralized integer linear programming (ILP) formulation is given under the assumption of the non-causal availability of exact and instantaneous inter-server latencies. The solution to this ILP can serve as a lower bound to benchmark more scalable and realistic schemes. Next, we give a distributed game-theoretic formulation (with service broker agents as players) which only requires the statistical knowledge of link latency fluctuations. The game provably admits a pure Nash equilibrium (NE) and can be solved iteratively through the well-known best response dynamics (BRD) algorithm. Our main novelty lies in the third formulation in which each service broker has neither instantaneous nor statistical knowledge of the latencies. Instead, it relies on a game-theoretic learning algorithm to compose its VNF chain only based on its own history of adopted decisions and experienced delays on each logical link. We prove that the proposed learning algorithm asymptotically converges to NE and evaluate its performance through simulations in terms of convergence and the impact of network parameters.

An accelerated Benders decomposition algorithm for the integrated storage space assignment, berth allocation, and yard crane deployment problem

In this paper, storage space assignment problem, berth allocation problem, and yard crane deployment problem are integrated with due attention to traffic congestion in the passing lines of storage yards. The paper provides a mixed-integer programming model for the integrated problem with the objective of minimizing the movement and operating costs of yard cranes, the cost of moving container flow between the storage yard and berths, and the delay cost of vessels. The planning horizon is considered to be one day and the berth layout is assumed to be discrete. Since the integrated storage space assignment and yard crane deployment problem is NP-hard and so is berth allocation problem, the integration of these three problems will be at least equally complex. Thus, an Accelerated Benders Decomposition Algorithm is also developed for solving the integrated problem. This is done by introducing a combinatorial Benders cut to the classical Benders decomposition algorithm to improve its performance and also adding 8 valid inequalities derived from the problem properties and assumptions to the algorithm formulation to accelerate the solution process. The proposed mathematical model and solution approach are evaluated by generating instances of different sizes for the problem and assessing the solution results. The results show the good performance of the proposed accelerated Benders decomposition algorithm. Taking the integrated approach to the modeling of the three problems rather than the non-integrated approach improved the cost of terminal operations by 11.15% on average.

Deployment Approach in WSNS Using Metaheuristic Algorithm: A Survey

Deploying wireless sensor networks (WSN) is one of the areas with the most significant research since the methods employed can have a big impact on the system's overall performance and the amount of energy the sensors in it consume.Thus, an effective deployment problem solution should not only "enhance its performance" but also save the energy needed to extend the lifetime of WSN. Finding an optimal solution for most deployment problems with limited computational resources is challenging, particularly for NP-hard optimization problems. By searching for a nearly optimal solution with constrained computational resources in a reasonable amount of time, metaheuristics present an alternative to exhaustive search and deterministic algorithms in solving these optimization problems. This paper classifies the deployment techniques of sensor networks and the metaheuristic algorithms.

Joint Deployment of Sensors and Chargers in Wireless Rechargeable Sensor Networks

As a promising technology to achieve the permanent operation of battery-powered wireless sensor devices, wireless rechargeable sensor networks (WRSNs) by radio-frequency radiation have attracted considerable attention in recent years. Determining how to save the deployment cost of WRSNs has been a hot topic. Previous scholars have mainly studied the cost of deploying chargers, thus ignoring the impact of sensor deployment on the network. Therefore, we consider the new problem of joint deployment of sensors and chargers on a two-dimensional plane, i.e., deploying the minimum number of sensors and chargers used to monitor points of interest (PoIs). Considering the interaction of deployed sensors and chargers, we divide the problem into two stages, P1 and P2. P1 addresses the sensor deployment, while P2 addresses the deployment of chargers. Both P1 and P2 have proved to be NP-hard. Meanwhile, we notice that the aggregation effect of sensors can effectively reduce the number of chargers deployed; therefore, we propose a greedy heuristic approximate solution for deploying sensors by using the aggregation effect (GHDSAE). Then, a greedy heuristic (GH) solution and a particle swarm optimization (PSO) solution are proposed for P2. The time complexity of these solutions is analyzed. Finally, extensive simulation results show that the PSO solution can always reduce the number of chargers deployed based on the GHDSAE solution sensor deployment approach. Therefore, it is more cost-effective to jointly deploy sensors and chargers by using the GHDSAE solution and the PSO solution.

Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme

The recent advancement in wireless power transmission (WPT) has led to the development of wireless rechargeable sensor networks (WRSNs), since this technology provides a means to replenish sensor nodes wirelessly, offering a solution to the energy challenges faced by WSNs. Most of the recent previous work has focused on charging sensor nodes using wireless charging vehicles (WCVs) equipped with high-capacity batteries and WPT devices. In these schemes, a vehicle can move close to a sensor node and wirelessly charge it without physical contact. While these schemes can mitigate the energy problem to some extent, they overlook two primary challenges of applied WCVs: off-road navigation and vehicle speed limitations. To overcome these challenges, previous work proposed a new WRSN model equipped with one drone coupled with several pads deployed to charge the drone when it cannot reach the subsequent stop. This wireless charging pad deployment aims to deploy the minimum number of pads so that at least one feasible routing path from the base station can be established for the drone to reach every SN in a given WRSN. The major weakness of previous studies is that they only consider deploying a wireless charging pad at the locations of the wireless sensor nodes. Their schemes are limited and constrained because usually every point in the deployed area can be considered to deploy a pad. Moreover, the deployed pads suggested by these schemes may not be able to meet the connected requirements due to sparse environments. In this work, we introduce a new scheme that utilizes the Quad-Tree concept to address the wireless charging pad deployment problem and reduce the number of deployed pads at the same time. Extensive simulations were conducted to illustrate the merits of the proposed schemes by comparing them with different previous schemes on maps of varying sizes. In the case of large maps, the proposed schemes surpassed all previous works, indicating that our approach is more suitable for large-scale network environments.

Solving Optimal Electric Vehicle Charger Deployment Problem

Electric vehicles (EVs) have already been acknowledged to be the most viable solution to the climate change that the entire globe has long been combating. Along the same line, it is a salient subject to expand the availability of EV charging infrastructure, which quintessentially necessitates the optimization of the charger’s locations. This paper proposes to formulate the optimal EV charger location problem into a facility location problem (FLP). As an effort to find an efficient method to solve the well-known nonpolynomial deterministic (NP) hard problem, we present a comparative quantification among several representative solving techniques. This paper features two comprehensive case studies representing regions with an average and a high density of EVs. As such, this paper shows that the proposed framework can lead to successful location optimization with adequate refinement of solving techniques.

Anomalously acting agents: the deployment problem

Anomalously acting agents: the deployment problem

Joint optimization of deployment, user association, channel, and resource allocation for fairness‐aware multi‐UAV network

AbstractThis paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle‐enabled access network. Since different user equipments performing different tasks and have different data rate requirements, the priority‐based traffic fairness problem is investigated. This problem, however, is a mixed integer nonlinear programming problem with NP‐hard complexity, making it challenging to be solved. To address this issue, a self‐organized and distributed framework “sense‐as‐you‐fly” based on the decomposition process, which divides the original problem into several subproblems, is proposed. Assuming without central controller, we derive the closed‐form resource allocation scheme and propose distributed many‐to‐one matching to optimize user association subproblem. Considering the coupled characteristics, the multi‐unmanned aerial vehicle deployment and channel allocation subproblems are modelled as a local altruistic game. The existence of Nash equilibrium is proved with the aid of exact potential game and efficient best response learning‐based algorithm is proposed. The original problem is finally addressed by solving the sub‐problems alternately and iteratively. Simulation results verify its effectiveness. By jointly optimizing multidimensional variables, the proposed algorithm unlocks network performance gains, especially in resource‐limited regimes.

Optimal deployment of private 5G multi-access edge computing systems at smart factories: Using hybrid crow search algorithm

In smart factories, an increasing number of mobile intelligent devices are deployed to meet the growing demands for flexible manufacturing. These devices, equipped with various sensors, synchronize a substantial amount of data with cloud servers for real-time monitoring and control. Fifth generation mobile networks (5G) combined with multi-access edge computing (MEC) provide the capabilities for multiple connections, large-scale data transmission, and efficient response times, becoming the mainstream network architectures for the needs. Additionally, with the emerging trend of energy-saving, recent works have addressed energy consumption as a significant cost factor. However, most previous studies tended to focus solely on cloud servers, neglecting energy consumption at edge computing and underestimating the impact of heterogeneous mobile device. As the scope of smart factory networks continues to expand, the challenges become increasingly critical. This study presents a private 5G MEC system architecture tailored for smart panel factories. The concerned problem is formulated as an integer programming model, which determines deployment locations and quantities for MEC servers and 5G small cells, including picocells and femtocells, so as to minimize the overall deployment costs while meeting the constraints of connectivity, capacity, latency, coverage, serviceability, and energy consumption. Since the edge server deployment problem is NP-hard, this study further proposes a hybrid metaheuristic algorithm, CSAVNS, which combines the strengths of crow search algorithm (CSA) and variable neighborhood search (VNS). In the global search phase of CSA, the VNS local search is introduced to enhance the algorithm's capabilities. Experimental analysis demonstrates that the proposed CSAVNS outperforms other algorithms in terms of solution solving capabilities.

Maximizing UAV fog deployment efficiency for critical rescue operations: A multi-objective optimization approach

In disaster scenarios and high-stakes rescue operations, integrating Unmanned Aerial Vehicles (UAVs) as fog nodes has become crucial. This integration ensures a smooth connection between affected populations and essential health monitoring devices, supporting the Internet of Things (IoT). Integrating UAVs in such environments is inherently challenging, where the primary objectives involve maximizing network connectivity and coverage while extending the networks lifetime through energy-efficient strategies to serve the maximum number of affected individuals. In this paper, we decomposed our problem into two subproblems. Connectivity, coverage subproblem and network lifespan optimization subproblem. For the Connectivity and coverage subproblem, the optimal connectivity and coverage are optimized by strategically deploying UAV fog nodes in a manner that maximizes network coverage, thoughtfully considering the distinctive constraints that emerge in these high-pressure situations where lives are at stake. We shape our UAV fog deployment problem as a multi-objective optimization and introduce a specialized UAV fog deployment algorithm tailored specifically for UAV fog nodes deployed in rescue missions. For the network lifespan subproblem, after determining the optimal connectivity and coverage of UAV nodes and users within the entire network, the network lifespan optimization subproblem is greatly simplified and efficiently solved via a one-dimensional swapping method. After conducting thorough experiments using our proposed architecture across diverse scenarios, our method consistently surpasses existing approaches. It effectively tackles issues like restricted connectivity and potential node failures. This advancement in deployment efficiency notably enhances rescue operations, enabling us to aid the maximum number of affected individuals swiftly during critical and time-pressing situations.